Ensemble machine learning models for sperm quality evaluation concerning success rate of clinical pregnancy in assisted reproductive techniques

This study aimed to investigate the influence of various sperm quality characteristics, including morphology, motility, and count, on the success rates of clinical pregnancy achieved through assisted reproductive technologies (ART) such as in-vitro fertilization (IVF), intracytoplasmic sperm injection (ICSI), and intrauterine insemination (IUI). The secondary objective was to assess the impact of these sperm parameters on the clinical pregnancy rate that resulted in the detection of a fetal heartbeat during the 11th week of gestation, a crucial milestone in successful ART-derived pregnancies. The researchers employed a retrospective analysis, evaluating data from 734 couples undergoing IVF/ICSI and 1197 couples undergoing IUI across two infertility centers. Exclusion criteria included cases involving donated eggs or sperm, surrogate uteri, and infertile couples with combined male and female factors. Five ensemble machine-learning models were utilized to predict the clinical pregnancy success rates. The Random Forest (RF) model achieved the highest mean accuracy (0.72) and area under the curve (AUC) (0.80), outperforming the other models for both IVF/ICSI and IUI procedures. The Shapley Additive Explanations (SHAP) value analysis revealed that for IUI cycles, all three sperm parameters (morphology, motility, and count) had significant negative impacts on the prediction of clinical pregnancy success. In contrast, for IVF/ICSI cycles, sperm motility had a positive effect, while sperm morphology and count were negative factors. In cycles with 1 to 5 retrieved eggs, sperm motility, and count, they positively affected the clinical pregnancy rate. The study also identified cut-off values for sperm count, with 54 and 35 being the respective thresholds for IVF/ICSI and IUI. Additionally, a significant cut-off point 30 was found for the sperm morphology parameter across all procedures. This study underscores the immense potential of leveraging ensemble machine learning models with traditional sperm quality assessments. This integrated approach can elevate the precision and personalization of clinical decision-making in the field of assisted reproductive technologies, ultimately offering more hope and better outcomes for couples struggling with infertility.